OpenRaiser/Intern-Atlas huggingface dataset:
Quantifying Point Contributions: A Lightweight Framework for Efficient and Effective Query-Driven Trajectory Simplification
Venue: arXiv cs.DB 2025
Year: 2025

As large volumes of trajectory data accumulate, simplifying trajectories to reduce storage and querying costs is increasingly studied. Existing proposals face three main problems. First, they require numerous iterations to decide which GPS points to delete. Second, they focus only on the relationships between neighboring points (local information) while neglecting the overall structure (global information), reducing the global similarity between the simplified and original trajectories and making it difficult to maintain consistency in query results, especially for similarity-based queries. Finally, they fail to differentiate the importance of points with similar features, leading to suboptimal selection of points to retain the original trajectory information. We propose MLSimp, a novel Mutual Learning query-driven trajectory simplification framework that integrates two distinct models: GNN-TS, based on graph neural networks, and Diff-TS, based on diffusion models. GNN-TS evaluates the importance of a point according to its globality, capturing its correlation with the entire trajectory, and its uniqueness, capturing its differences from neighboring points. It also incorporates attention mechanisms in the GNN layers, enabling simultaneous data integration from all points within the same trajectory and refining representations, thus avoiding iterative processes. Diff-TS generates amplified signals to enable the retention of the most important points at low compression rates. Experiments involving eight baselines on three databases show that MLSimp reduces the simplification time by 42%--70% and improves query accuracy over simplified trajectories by up to 34.6%.
V$^2$Dial: Unification of Video and Visual Dialog via Multimodal Experts
Venue: arXiv cs.CV 2025
Year: 2025

We present V$^2$Dial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multimodal models primarily focus on simpler tasks (e.g., VQA, VideoQA, video-text retrieval) and often neglect the more challenging conversational counterparts, such as video and visual/image dialog. Moreover, works on both conversational tasks evolved separately from each other despite their apparent similarities limiting their applicability potential. To this end, we propose to unify both tasks using a single model that for the first time jointly learns the spatial and temporal features of images and videos by routing them through dedicated experts and aligns them using matching and contrastive learning techniques. Furthermore, we systemically study the domain shift between the two tasks by investigating whether and to what extent these seemingly related tasks can mutually benefit from their respective training data. Extensive evaluations on the widely used video and visual dialog datasets of AVSD and VisDial show that our model achieves new state-of-the-art results across four benchmarks both in zero-shot and fine-tuning settings.
Superscopes: Amplifying Internal Feature Representations for Language Model Interpretation
Venue: arXiv cs.CL 2025
Year: 2025

Understanding and interpreting the internal representations of large language models (LLMs) remains an open challenge. Patchscopes introduced a method for probing internal activations by patching them into new prompts, prompting models to self-explain their hidden representations. We introduce Superscopes, a technique that systematically amplifies superposed features in MLP outputs (multilayer perceptron) and hidden states before patching them into new contexts. Inspired by the "features as directions" perspective and the Classifier-Free Guidance (CFG) approach from diffusion models, Superscopes amplifies weak but meaningful features, enabling the interpretation of internal representations that previous methods failed to explain-all without requiring additional training. This approach provides new insights into how LLMs build context and represent complex concepts, further advancing mechanistic interpretability.
A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub
Venue: arXiv cs.DB 2025
Year: 2025

In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning heterogeneous data under standardized representations, benefiting both repository curators responsible for upholding data quality standards and consumers who require unified datasets. However, data harmonization is difficult in practice, requiring significant domain and technical expertise. We present a software framework to facilitate principled and reproducible harmonization protocols. Our framework implements a novel strategy of building harmonization transformations from parameterizable primitive operations, such as the assignment of numerical values to user-specified categories, with automated bookkeeping for executed transformations. We establish our data representation model and harmonization strategy and then report a proof-of-concept application in the context of the RADx Data Hub. Our framework enables data practitioners to execute transparent and reproducible harmonization protocols that align closely with their research goals.
Does the Story Matter? Applying Narrative Theory to an Educational Misinformation Escape Room Game
Venue: arXiv cs.HC 2025
Year: 2025

Rapid spread of harmful misinformation has led to a dire need for effective media literacy interventions, to which educational games have been suggested as a possible solution. Researchers and educators have created several games that increase media literacy and resilience to misinformation. However, the existing body of misinformation education games rarely focus upon the socio-emotional influences that factor into misinformation belief. Misinformation correction and serious games have both explored narrative as a method to engage with people on an emotional basis. To this end, we investigated how 123 young adults (mean age = 22.98) experienced narrative transportation and identification in two narrative-centered misinformation escape room games developed for library settings. We found that propensity for certain misinformation contexts, such as engagement with fan culture and likelihood to share on social media platforms, significantly affected how participants experienced specific measures of narrative immersion within the games. We discuss design implications for tailoring educational interventions to specific misinformation contexts.
Measuring Intrinsic Dimension of Token Embeddings
Venue: arXiv cs.CL 2025
Year: 2025

In this study, we measure the Intrinsic Dimension (ID) of token embedding to estimate the intrinsic dimensions of the manifolds spanned by the representations, so as to evaluate their redundancy quantitatively compared to their extrinsic dimensionality. In detail, (1) we estimate the ID of token embeddings in small-scale language models and also modern large language models, finding that the embedding spaces often reside on lower-dimensional manifolds compared to their extrinsic dimensionality; (2) we measure the ID across various model sizes and observe an increase in redundancy rates as the model scale grows; (3) we measure the dynamics of IDs during the training process, and find a rapid ID drop in the early stages of training. Moreover, (4) when LoRA is applied to the embedding layers, we observe a sudden drop in perplexity around the estimated IDs, suggesting that the ID can serve as a useful guideline for LoRA application.
Trust and Friction: Negotiating How Information Flows Through Decentralized Social Media
Venue: arXiv cs.HC 2025
Year: 2025

Decentralized social media protocols enable users in independent, user-hosted servers (i.e., instances) to interact with each other while they self-govern. This community-based model of social media governance opens up new opportunities for tailored decision-making about information flows -- i.e., what user data is shared to whom and when -- and in turn, for protecting user privacy. To better understand how community governance shapes privacy expectations on decentralized social media, we conducted a semi-structured interview with 23 users of the Fediverse, a decentralized social media network. Our findings illustrate important factors that shape a community's understandings of information flows, such as rules and proactive efforts from admins who are perceived as trustworthy. We also highlight ''governance frictions'' between communities that raise new privacy risks due to incompatibilities in values, security practices, and software. Our findings highlight the unique challenges of decentralized social media, suggest design opportunities to address frictions, and outline the role of participatory decision-making to realize the full potential of decentralization.
YouthCare: Building a Personalized Collaborative Video Censorship Tool to Support Parent-Child Joint Media Engagement
Venue: arXiv cs.HC 2025
Year: 2025

To mitigate the negative impacts of online videos on teenagers, existing research and platforms have implemented various parental mediation mechanisms, such as Parent-Child Joint Media Engagement (JME). However, JME generally relies heavily on parents' time, knowledge, and experience. To fill this gap, we aim to design an automatic tool to help parents/children censor videos more effectively and efficiently in JME. For this goal, we first conducted a formative study to identify the needs and expectations of teenagers and parents for such a system. Based on the findings, we designed YouthCare, a personalized collaborative video censorship tool that supports parents and children to collaboratively filter out inappropriate content and select appropriate content in JME. An evaluation with 10 parent-child pairs demonstrated YouthCare's several strengths in supporting video censorship, while also highlighting some potential problems. These findings inspire us to propose several insights for the future design of parent-child collaborative JME systems.
MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain, yet Medical LVLMs (Med-LVLMs) frequently generate hallucinations due to limited expertise and the complexity of medical applications. Existing benchmarks fail to effectively evaluate hallucinations based on their underlying causes and lack assessments of mitigation strategies. To address this gap, we introduce MedHEval, a novel benchmark that systematically evaluates hallucinations and mitigation strategies in Med-LVLMs by categorizing them into three underlying causes: visual misinterpretation, knowledge deficiency, and context misalignment. We construct a diverse set of close- and open-ended medical VQA datasets with comprehensive evaluation metrics to assess these hallucination types. We conduct extensive experiments across 11 popular (Med)-LVLMs and evaluate 7 state-of-the-art hallucination mitigation techniques. Results reveal that Med-LVLMs struggle with hallucinations arising from different causes while existing mitigation methods show limited effectiveness, especially for knowledge- and context-based errors. These findings underscore the need for improved alignment training and specialized mitigation strategies to enhance Med-LVLMs' reliability. MedHEval establishes a standardized framework for evaluating and mitigating medical hallucinations, guiding the development of more trustworthy Med-LVLMs.
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning
Venue: arXiv cs.CV 2025
Year: 2025

Computed tomography (CT) is a key imaging modality for diagnosis, yet its clinical utility is marred by high radiation exposure and long turnaround times, restricting its use for larger-scale screening. Although chest radiography (CXR) is more accessible and safer, existing CXR foundation models focus primarily on detecting diseases that are readily visible on the CXR. Recently, works have explored training disease classification models on simulated CXRs, but they remain limited to recognizing a single disease type from CT. CT foundation models have also emerged with significantly improved detection of pathologies in CT. However, the generalized application of CT-derived labels on CXR has remained illusive. In this study, we propose X2CT-CLIP, a tri-modal knowledge transfer learning framework that bridges the modality gap between CT and CXR while reducing the computational burden of model training. Our approach is the first work to enable multi-abnormality classification in CT, using CXR, by transferring knowledge from 3D CT volumes and associated radiology reports to a CXR encoder via a carefully designed tri-modal alignment mechanism in latent space. Extensive evaluations on three multi-label CT datasets demonstrate that our method outperforms state-of-the-art baselines in cross-modal retrieval, few-shot adaptation, and external validation. These results highlight the potential of CXR, enriched with knowledge derived from CT, as a viable efficient alternative for disease detection in resource-limited settings.
Relaxation for Efficient Asynchronous Queues
Venue: arXiv cs.DC 2025
Year: 2025

We explore the problem of efficiently implementing shared data structures in an asynchronous computing environment. We start with a traditional FIFO queue, showing that full replication is possible with a delay of only a single round-trip message between invocation and response of each operation. This is optimal, or near-optimal, runtime for the Dequeue operation. We then consider ways to circumvent this limitation on performance. Though we cannot improve the worst-case time per operation instance, we show that relaxation, weakening the ordering guarantees of the Queue data type, allows most Dequeue instances to return after only local computation, giving a low amortized cost per instance. This performance is tunable, giving a customizable tradeoff between the ordering of data and the speed of access
Is Bellman Equation Enough for Learning Control?
Venue: arXiv cs.LG 2025
Year: 2025

The Bellman equation and its continuous-time counterpart, the Hamilton-Jacobi-Bellman (HJB) equation, serve as necessary conditions for optimality in reinforcement learning and optimal control. While the value function is known to be the unique solution to the Bellman equation in tabular settings, we demonstrate that this uniqueness fails to hold in continuous state spaces. Specifically, for linear dynamical systems, we prove the Bellman equation admits at least $\binom{2n}{n}$ solutions, where $n$ is the state dimension. Crucially, only one of these solutions yields both an optimal policy and a stable closed-loop system. We then demonstrate a common failure mode in value-based methods: convergence to unstable solutions due to the exponential imbalance between admissible and inadmissible solutions. Finally, we introduce a positive-definite neural architecture that guarantees convergence to the stable solution by construction to address this issue.
Adaptive Traffic Signal Control based on Multi-Agent Reinforcement Learning. Case Study on a simulated real-world corridor
Venue: arXiv cs.MA 2025
Year: 2025

Previous studies that have formulated multi-agent reinforcement learning (RL) algorithms for adaptive traffic signal control have primarily used value-based RL methods. However, recent literature has shown that policy-based methods may perform better in partially observable environments. Additionally, RL methods remain largely untested for real-world normally signal timing plans because of the simplifying assumptions common in the literature. The current study attempts to address these gaps and formulates a multi-agent proximal policy optimization (MA-PPO) algorithm to implement adaptive and coordinated traffic control along an arterial corridor. The formulated MA-PPO has a centralized-critic architecture under a centralized training and decentralized execution framework. Agents are designed to allow selection and implementation of up to eight signal phases, as commonly implemented in field controllers. The formulated algorithm is tested on a simulated real-world seven intersection corridor. The speed of convergence for each agent was found to depend on the size of the action space, which depends on the number and sequence of signal phases. The performance of the formulated MA-PPO adaptive control algorithm is compared with the field implemented actuated-coordinated signal control (ASC), modeled using PTV-Vissim-MaxTime software in the loop simulation (SILs). The trained MA-PPO performed significantly better than the ASC for all movements. Compared to ASC the MA-PPO showed 2% and 24% improvements in travel time in the primary and secondary coordination directions, respectively. For cross streets movements MA-PPO also showed significant crossing time reductions. Volume sensitivity experiments revealed that the formulated MA-PPO demonstrated good stability, robustness, and adaptability to changes in traffic demand.
Making Better Mistakes in CLIP-Based Zero-Shot Classification with Hierarchy-Aware Language Prompts
Venue: arXiv cs.CV 2025
Year: 2025

Recent studies are leveraging advancements in large language models (LLMs) trained on extensive internet-crawled text data to generate textual descriptions of downstream classes in CLIP-based zero-shot image classification. While most of these approaches aim at improving accuracy, our work focuses on ``making better mistakes", of which the mistakes' severities are derived from the given label hierarchy of downstream tasks. Since CLIP's image encoder is trained with language supervising signals, it implicitly captures the hierarchical semantic relationships between different classes. This motivates our goal of making better mistakes in zero-shot classification, a task for which CLIP is naturally well-suited. Our approach (HAPrompts) queries the language model to produce textual representations for given classes as zero-shot classifiers of CLIP to perform image classification on downstream tasks. To our knowledge, this is the first work to introduce making better mistakes in CLIP-based zero-shot classification. Our approach outperforms the related methods in a holistic comparison across five datasets of varying scales with label hierarchies of different heights in our experiments. Our code and LLM-generated image prompts: \href{https://github.com/ltong1130ztr/HAPrompts}{https://github.com/ltong1130ztr/HAPrompts}.
Real-Time Burst-Mode Digital Signal Processing for Passive Optical Networks
Venue: arXiv cs.NI 2025
Year: 2025

Driven by the ever-increasing capacity demands, the 50G passive optical network (PON) is maturing gradually. One of the main challenges for the 50G PON is implementing burst-mode digital signal processing (BM-DSP) for the burst upstream signal. In this paper, we demonstrate a real-time BM-DSP for burst reception of 25Gbit/s on-off keying signal to meet the asymmetric-mode 50G PON demand. The real-time BM-DSP includes the BM frequency-domain timing recovery and BM frequency-domain equalizer, which can be fast converged based on the 42ns designed preamble. Meanwhile, the simplified implementations for fast-Fourier-transform, minimum-mean-square-error, and decision-directed least-mean-square-error algorithms decrease the DSP resources by 28.57%, enabling the loading of real-time BM-DSP in the field programmable gate array with the limited DSP resources. The real-time implementation of BM-DSP can guide the design of application-specific integrated circuits for 50G PON.
Boosting Rectilinear Steiner Minimum Tree Algorithms with Augmented Bounding Volume Hierarchy
Venue: arXiv cs.DS 2025
Year: 2025

The rectilinear Steiner minimum tree (RSMT) problem computes the shortest network connecting a given set of points using only horizontal and vertical lines, possibly adding extra points (Steiner points) to minimize the total length. RSMT solvers seek to balance speed and accuracy. In this work, we design a framework to boost existing RSMT solvers, extending the Pareto front. Combined with GeoSteiner, our algorithm reaches 5.16\% length error on nets with 1000 pins. The average time needed is 0.46 seconds. This provides an effective way to solve large-scale RSMT problems with small-scale solvers.
A differential model of $N$ player games concerning ethical dilemmas
Venue: arXiv cs.GT 2025
Year: 2025

Ethics play an important role in determining the behavior of an individual under certain circumstances. Ethical or unethical behavior can be treated as a strategy of a player in a pay-off game. In this paper, we present two analytical solutions to studying time evolution of behavior of an individual from ethics perspective. We also present the effect of a third player as a perturbation to a two player game and develop a general approach for a $N$ player game. We demonstrate geometric modeling of behavioral characteristics of individuals as polytopes residing in $D$ dimensional space. We treat three player and two player games using set of differential equations that lead to time evolution of phase trajectories which reveal about the interdependencies and self dependencies of each player. We also demonstrate the effect of strategies of each player on other players in cardinal games.
Enhancing the Product Quality of the Injection Process Using eXplainable Artificial Intelligence
Venue: arXiv cs.AI 2025
Year: 2025

The injection molding process is a traditional technique for making products in various industries such as electronics and automobiles via solidifying liquid resin into certain molds. Although the process is not related to creating the main part of engines or semiconductors, this manufacturing methodology sets the final form of the products. Re-cently, research has continued to reduce the defect rate of the injection molding process. This study proposes an optimal injection molding process control system to reduce the defect rate of injection molding products with XAI (eXplainable Artificial Intelligence) ap-proaches. Boosting algorithms (XGBoost and LightGBM) are used as tree-based classifiers for predicting whether each product is normal or defective. The main features to control the process for improving the product are extracted by SHapley Additive exPlanations, while the individual conditional expectation analyzes the optimal control range of these extracted features. To validate the methodology presented in this work, the actual injection molding AI manufacturing dataset provided by KAMP (Korea AI Manufacturing Platform) is employed for the case study. The results reveal that the defect rate decreases from 1.00% (Original defect rate) to 0.21% with XGBoost and 0.13% with LightGBM, respectively.